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External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times.
There are five key principles relating to internal validity (study design) and external validity (generalizability) which rigorous impact evaluations should address: confounding factors, selection bias, spillover effects, contamination, and impact heterogeneity. [5]
The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
In qualitative research, a member check, also known as informant feedback or respondent validation, is a technique used by researchers to help improve the accuracy, credibility, validity, and transferability (also known as applicability, internal validity, [1] or fittingness) of a study. [2]
A true experiment would, for example, randomly assign children to a scholarship, in order to control for all other variables. Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.
Ecological validity, the ability to generalize study findings to the real world, is a subcategory of external validity. [6] Another example highlighting the differences between these terms is from an experiment that studied pointing [7] —a trait originally attributed uniquely to humans—in captive chimpanzees. This study certainly had ...
While the general arguments in the paper recommending reforms in scientific research methodology were well-received, Ionnidis received criticism for the validity of his model and his claim that the majority of scientific findings are false. Responses to the paper suggest lower false positive and false negative rates than what Ionnidis puts forth.
Correlations that fit the expected pattern contribute evidence of construct validity. Construct validity is a judgment based on the accumulation of correlations from numerous studies using the instrument being evaluated. [22] Most researchers attempt to test the construct validity before the main research. To do this pilot studies may be ...